It is now well established that many complex human disorders and quantitative traits have a substantial genetic component. Consequently, many investigators have initiated major efforts to uncover specific genetic factors influencing these traits. This is generating a wealth of data that requires sophisticated statistical analysis to maximize the information gleaned. Likewise, numerous advances in statistical methods for the study of complex genetic traits have been made including: multipoint mapping, multivariate linkage analysis, multilocus analysis, variance components and other analytic methods, modeling of sibpair covariances with robust least squares methods, transmission disequilibrium testing for quantitative traits, linkage disequilibrium mapping, drift mapping, mapping by admixture linkage disequilibrium, etc. No current course is available for applied investigators and data analysts working primarily with quantitative traits such as those underlying the areas of obesity, diabetes, dyslipidemias, hypertension, osteoporosis, and other nutritionally-related conditions to receive instruction in statistical genetic methods and have the instruction geared specifically toward their needs. The PI proposes such a course. The course will be taught by leading experts in statistical genetics. "Students" of the course will be established investigators, post-doctoral fellows, and advanced graduate students working actively on the genetics of complex quantitative traits. It will be offered to advanced graduate students working actively on the genetics of complex quantitative traits. It will be offered in two forms on alternating years. One will be a 2-day course in conjunction with a national conference. The 2-day course will be "concept-based" as opposed to "hands-on" and aimed at the level of the established non-statistician investigator. On alternate years, the course will be offered in a longer 4-day format. The 4-day course will be heavily computer "hands-on" and aimed at the level of the applied statistician or data analyst. Approximately 35 students will attend each course, allowing for intensive interaction between students and faculty. At each course, lectures will be supplemented with extensive discussion sessions, handouts, presentation of worked examples, and interactive demonstrations of statistical genetic data analyses.